“This idea provides an inequality that is used in our risk budgeting formulation, namely that each option’s ex-ante alpha needs to exceed a minimum hurdle to justify a departure from beta allocations.” The work undertaken by UniSuper is built up from prior work on risk budgeting – including that of Mena (2007), Litterman (2003), Scherer (2000), Kozun (2001) and Sharpe (2002). However the authors extended these findings by, amongst other things, removing the simplifying assumption that excess returns between managers are uncorrelated; introducing the idea that to justify active risk, one needs to exceed a hurdle return in excess of 0 per cent.

In order to meet these objectives six processes were outlined to be computed by TURBOs: 1. Attributes each manager’s returns between a series of market factor exposures (beta) and an observed expost (historic) alpha component. This step is resolved using factor analysis and multiple regression, with appropriate adjustments to manage collinearity, heteroskedasticity and co-integration 2. Determines the ex-post total risk (volatility) and tracking error, and assesses the marginal and proportional contribution to that risk from each manger 3. Uses Bayesian techniques to determine an ex-ante estimate of each manager’s alpha 4. Assesses the extent to which the beat exposure differs to the fund’s SAA benchmarks 5. Uses risk budgeting techniques to set a minimum excess return hurdle at which active risk is appropriate and assess the extent to which the hurdle is expected to be achieved 6. Employs reverse optimisation to confirm whether the active risk assigned to each of the fund’s managers is consistent with the expected performance of the manager.

 

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